Face photo-drawing conversion based on multi-scale feature-enhanced generative adversarial networks

ABSTRACT

This paper introduces a novel face photo-to-sketch synthesis method using a multi-scale feature-enhanced generative adversarial network (MFEGAN). The MFEGAN framework captures features at various scales through a multi-scale feature extraction module, enhanced by an attention mechanism. An improved attention residual block in the generator adaptively refines deep image features, improving overall quality. A pre-trained feature extraction network extracts and fuses face-specific features, enriching identity information. Multi-scale perceptual and focal frequency losses optimize detail quality, aligning with human perception. Experimental results show that MFEGAN outperforms existing methods in visual appeal and fidelity to original identity features.

Index Terms:

• face photo-sketch conversion
• generative adversarial network
• attention mechanism
• multi-scale feature extraction
• deep learning

Acknowledgements

The authors want to acknowledge the financial support from the National Natural Science Foundation of China(Project No.:62362063.61866037).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number: 62362063].